mdtw.py

from math import *
import numpy as np
import sys
from python_speech_features import mfcc
from python_speech_features import delta
from python_speech_features import logfbank
import scipy.io.wavfile as wav
# def mfcc(signal,samplerate=16000,winlen=0.025,winstep=0.01,numcep=13,
                 # nfilt=26,nfft=1103,lowfreq=0,highfreq=None,preemph=0.97,
     # ceplifter=22,appendEnergy=True)

(rate1,sig1) = wav.read("testaudio1.wav")
mfcc_feat1 = mfcc(sig1,rate1)
d_mfcc_feat1 = delta(mfcc_feat1, 2)
fbank_feat1 = logfbank(sig1,rate1)

(rate2,sig2) = wav.read("testaudio2.wav")
mfcc_feat2 = mfcc(sig2,rate2)
d_mfcc_feat2 = delta(mfcc_feat2, 2)
fbank_feat2 = logfbank(sig2,rate2)
# def DTW(A, B, window = sys.maxint, d = lambda x,y: abs(x-y)):
    # A, B = np.array(A), np.array(B)
AX,AY = fbank_feat1.shape
BX,BY = fbank_feat2.shape
cost = sys.maxint * np.ones((AX,BX,BY))
print cost.shape

cost[0,0,0] = d(A[0][0], B[0][0])
#     for i in range(1, M):
#         cost[i, 0, 0] = cost[i-1, 0, 0] + d(A[i][0], B[0][0])
#
#     for j in range(1, N):
#         cost[0, j] = cost[0, j-1] + d(A[0], B[j])
#
#     for i in range(1, M):
#         for j in range(max(1, i - window), min(N, i + window)):
#             choices = cost[i - 1, j - 1], cost[i, j-1], cost[i-1, j]
#             cost[i, j] = min(choices) + d(A[i], B[j])
#
#
#     n, m = N - 1, M - 1
#     path = []
#
#     while (m, n) != (0, 0):
#         path.append((m, n))
#         m, n = min((m - 1, n), (m, n - 1), (m - 1, n - 1), key = lambda x: cost[x[0], x[1]])
#
#     path.append((0,0))
#     return cost[-1, -1], path
#
# def main():
#     A = fbank_feat1[:1]
#     B = fbank_feat2 [:1]
#     # A = np.random.rand(100,1)
#     # B = A
#     # C = np.array([7,8,5,9,11,9])
#     # B = C
#     cost, path = DTW(A, B, window = 4)
#     print 'Total Distance is ', cost
#     import matplotlib.pyplot as plt
#     offset = 5
#     plt.xlim([-1, max(len(A), len(B)) + 1])
#     plt.plot(A)
#     plt.plot(B + offset)
#     for (x1, x2) in path:
#         plt.plot([x1, x2], [A[x1], B[x2] + offset])
#     plt.show()

# if __name__ == '__main__':
#     main()